Teams adopting Claude, Kiro, or Cursor who want speed without losing the plot.
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AI Workflow Architecture
Turn AI from a chaotic autocomplete gremlin into a repeatable engineering workflow.
This is not “I'll prompt ChatGPT for you.” It's architecture for AI-assisted development, content, and automation systems — the steering files, validation loops, review gates, and repeatable prompting that let AI make you faster without quietly surrendering your architecture.
Who it's for
Who this is for
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Founders shipping with AI who keep getting plausible-looking output they can't fully trust.
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Engineering leads who need AI use to stay reviewable, safe, and consistent across a team.
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Solo builders drowning in half-working AI output and ready for an actual workflow.
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Content or automation teams using AI at volume who need guardrails, not vibes.
Problems
Problems this solves
The failure modes that show up once AI is in the loop and nothing's holding it accountable.
- AI confidently produces spaghetti — code or content that looks right and rots fast.
- Everyone prompts differently, so output quality is a coin flip.
- There are no review gates, so AI changes land unchecked and break things quietly.
- Secrets, context, and repo rules leak into prompts — or get ignored entirely.
- Giant AI changes nobody can realistically review, instead of small validated slices.
- AI gets treated as an oracle instead of a fast, fallible assistant that needs checking.
The work
What I help design
The moving parts of an AI workflow that stays fast, reviewable, and safe.
Agent workflows
What the agent is allowed to do, where it hands back to a human, and how work flows end to end.
Steering & instruction files
Repo-safe instructions (CLAUDE.md / AGENTS.md-style) that keep AI on-pattern instead of improvising.
Validation loops
check / build / test gates the AI has to pass before anything counts as done.
PR & slice workflows
Small, reviewable pull requests instead of giant unreviewable dumps of generated work.
Human review gates
Explicit points where a person signs off before changes touch anything that matters.
Repeatable prompting systems
Reusable request templates so output quality doesn't depend on who's typing today.
Repo-safe guardrails
Secrets handling, scope limits, and clear “don't touch these” rules baked into the workflow.
Anti-spaghetti architecture
Boundaries and conventions that keep AI-generated work coherent as it scales.
Deliverables
Example deliverables
Concrete artifacts you keep and operate — not a slide deck.
- A steering-file set (e.g. CLAUDE.md / AGENTS.md) tuned to your repo and conventions.
- A documented agent / PR / slice workflow your team can actually follow.
- Reusable prompt and request templates for your common tasks.
- Validation-loop wiring — the check/build/test gates AI output must pass.
- A review-gate checklist defining what a human approves, and when.
- A short write-up of what to do — and what to never let the AI do.
Process
How engagements usually work
No bloated proposals or account managers. A typical engagement runs in four steps.
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Start with a note
Send the messy version of the problem. We figure out whether it's even a fit before anything formal.
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Scope a slice
We agree on a small, well-bounded first piece — what's in, what's out, and what “done” actually means.
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Build and validate
I work in small, reviewable steps, validate as I go, and keep you in the loop instead of vanishing for a month.
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Hand off something you can operate
You end up with a working system and the docs to run it — not a black box only I understand.
Fit
Good fit / not a good fit
I'd rather be honest up front than oversell. AI is a sharp tool, not a magic wand.
Good fit
- You want AI to make engineering faster and more consistent, not replace judgment.
- Reviewability, security, and maintainable output actually matter to you.
- You're happy to work in small, validated slices.
- You want a system the whole team can follow, not a clever one-off prompt.
Not a fit
- You want a guaranteed “10x overnight” or fully hands-off autonomous AI.
- You want AI to replace engineers and skip human review entirely.
- You're after a generic AI-agency pitch deck instead of a working setup.
- Validation, security, or review are seen as optional speed bumps.
Go deeper
Related writing, resources & projects
The thinking and the receipts behind this work.
Next step
Make AI useful without surrendering architecture
Tell me how your team — or future-you — is using AI today, and where it's getting messy. We'll design the workflow that keeps the speed and loses the chaos.